Systems for detecting and analyzing target patterns in digital imagery have a wide variety of uses. Such systems can be used to detect airplanes, ships, submarines and even schools of fish using radar and sonar. Pattern recognition systems are also used to detect geographical objects, military targets and weather patterns from satellite images. Conventional pattern recognition systems use a template of the object that is to be detected. For example, a satellite image of the ocean is searched for an object that matches a template of a particular ship.
An increasingly important area is the detection and analysis of anatomical regions in the human body. For example, radiological images from computed tomography (CT) are used for the computer-aided detection (CAD) of various ailments in human organs. Images from magnetic resonance imaging (MRI) are also used in computer-aided detection. For the detection and diagnosis of the ailments in one human organ, it is often helpful to identify the surrounding organs as well. Consequently, an “anatomical model” of a patient is generated in which many of the patient's internal organs are identified on digital image slices of the patient.
Conventionally, pixels in multiple digital image slices are compared to a three-dimensional template of a target organ that is to be detected. The pixels that are associated with the target organ are identified based on their properties, such as brightness. The templates of the target organs are expanded, contracted and generally modified in order to obtain a match with a three-dimensional object in the digital image slices. After a match is found, the conventional pattern recognition system fills out the organ by growing the identified boundary of the organ into pixels having a similar brightness or texture. For each digital image slice, the mask is placed in the appropriate region using expert knowledge so that the desired organ can be identified. A conventional pixel-based pattern recognition system generates anatomical models only with the supervision of an expert and, therefore, has several disadvantages. First, the interactive guidance of an expert is costly and not always available. Second, the accuracy of detecting particular organs depends on the knowledge of the particular expert, and inconsistent results are obtained. Third, the rules by which an organ boundary is made to grow into pixels with similar properties do not apply equally well to images acquired using different machines that might have variations in the thickness of the digital image slices, as well as different picture qualities and variations in pixel brightness. Finally, a conventional pattern recognition system identifies each organ independently by matching an object in an image to a template and does not use the relationships between the organs for the recognition except through the interactive guidance of an expert.
An improved CAD scheme is sought for automatically generating an anatomical model of a patient without the manual application of expert knowledge. Moreover, such an improved CAD scheme would use the relationships between organs to identify those organs and to generate an anatomical model.